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» Efficient Inference in Persistent Dynamic Bayesian Networks
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NIPS
2007
13 years 9 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
IEEEARES
2008
IEEE
14 years 2 months ago
A Bayesian Approach for on-Line Max Auditing
In this paper we propose a method for on-line max auditing of dynamic statistical databases. The method extends the Bayesian approach presented in [2], [3] and [4] for static data...
Gerardo Canfora, Bice Cavallo
IJON
2010
138views more  IJON 2010»
13 years 6 months ago
A dynamic Bayesian network to represent discrete duration models
Originally devoted to specific applications such as biology, medicine and demography, duration models are now widely used in economy, finance or reliability. Recent works in var...
Roland Donat, Philippe Leray, Laurent Bouillaut, P...
NIPS
2004
13 years 9 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Pradeep Shenoy, Rajesh P. N. Rao
IROS
2007
IEEE
125views Robotics» more  IROS 2007»
14 years 1 months ago
Probabilistic inference for structured planning in robotics
Abstract— Real-world robotic environments are highly structured. The scalability of planning and reasoning methods to cope with complex problems in such environments crucially de...
Marc Toussaint, Christian Goerick